Identification of mitochondrial disease genes through integrative analysis of multiple datasets.
Determining the genetic factors in a disease is crucial to elucidating its molecular basis. This task is challenging due to a lack of information on gene function. The integration of large-scale functional genomics data has proven to be an effective strategy to prioritize candidate disease genes. Mitochondrial disorders are a prevalent and heterogeneous class of diseases that are particularly amenable to this approach. Here we explain the application of integrative approaches to the identification of mitochondrial disease genes. We first examine various datasets that can be used to evaluate the involvement of each gene in mitochondrial function. The data integration methodology is then described, accompanied by examples of common implementations. Finally, we discuss how gene networks are constructed using integrative techniques and applied to candidate gene prioritization. Relevant public data resources are indicated. This report highlights the success and potential of data integration as well as its applicability to the search for mitochondrial disease genes.
Aiyar
Raeka S
RS
European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.
Gagneur
Julien
J
Steinmetz
Lars M
LM
D020561:Alphaproteobacteria / Q000235:genetics; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D050259:Genes, Mitochondrial; D006801:Humans; D008928:Mitochondria / Q000378:metabolism; D028361:Mitochondrial Diseases / Q000235:genetics*; D025941:Protein Interaction Mapping; D040901:Proteomics / Q000379:methods